【Sakana.AI】David Ha氏の論文解説【さかなAI】

The video begins by addressing the lack of new information in a recent news article about Fish AI, which is essentially unchanged from two months prior. The presenter expresses a desire to introduce a paper on military intelligence written by David Hahs, which is believed to have been written last year. The video aims to clarify what kind of AI Fish AI is attempting to create. Fish AI, named after the collective behavior of fish schools, is an AI enterprise co-founded by former Google employees, including David Hahs, who is one of the authors of the influential 'Attention Is All You Need' paper. The goal of Fish AI is to develop a flexible model that adapts to environmental changes, inspired by the simple rules of fish schooling. The presenter also discusses the limitations of current AI systems like ChatGPT, which are designed to be unchanging and unresponsive to environmental changes, unlike natural systems that are sensitive to their surroundings and exhibit collective intelligence.

The video then delves into the concept of incorporating collective intelligence into deep learning. It mentions that while deep learning models like ChatGPT are powerful, they lack the adaptability seen in natural systems. The presenter introduces the idea of Cellular Neural Networks (CNN), which were developed in the 1980s and are similar to the human brain's structure, allowing for complex interactions between neurons. CNNs were overshadowed by the rise of GPUs and the simplicity of deep learning models, which rely on simple computations repeated at high speed. The video suggests that incorporating the principles of CNNs could lead to more adaptable AI systems. Examples include image processing, where CNNs can predict colors based on adjacent pixels, making the system robust against noise and capable of image regeneration even with damaged data. The concept is also applicable to 3D data, allowing for the reconstruction of accurate 3D models from sparse and incomplete point clouds by leveraging information from surrounding data.

The final paragraph discusses advanced research in AI, including the use of collective intelligence in deep reinforcement learning and multi-agent learning. It explains how traditional reinforcement learning involves training a single agent, like a character in a video game, to achieve a goal through trial and error. In contrast, collective intelligence-based learning involves multiple agents learning from each other, adapting their actions to achieve a common goal, much like cells in a multicellular organism. The video also touches on multi-agent learning platforms like Neural MMO, which allow for the observation of learning behaviors in a large number of agents competing for limited resources. Lastly, the presenter introduces meta-learning, which is about training the system to learn. Meta-learning aims to create AI systems that can quickly adapt to new tasks without needing to be retrained from scratch, potentially leading to the creation of AI that can think and learn independently.